Communicating Data
Vincenzo Coia
About me
- PhD in Statistics at UBC
- Assistant Professor of Teaching in stats
- “Bridging the gap between data science and statistics”
- Long-time TA for SCIE 300
- Outdoor enthusiast.
Outline for Today
Part 1
Visualizing different types of data
Part 2
Principles for making effective plots
Attribution
Ideas are from “Fundamentals of Data Visualization” by Claus Wilke. You should check it out!
Slides are my own.
class: middle, center
data-to-viz
Visualizing Amounts
Use bars
Visualizing Amounts
Use bars. Sensibly rearrange.
In this case: movie order and descending order.
Visualizing Amounts
Bars must go to zero: we interpret area. Don’t do this:
Visualizing Amounts
When zero doesn’t matter, use points:
Visualizing Amounts
x-labels too big? Don’t be afraid to swap axes.
Visualizing Amounts
Sorting, again.
Visualizing Distributions
Want to compare body mass of three penguin species? Please don’t use pinhead plots.
Visualizing Distributions
Plot all the data instead.
Visualizing Distributions
Even better, add some jitter and alpha transparency:
Visualizing Distributions: too much data to show
Too much data to show? Could use boxplots:
Visualizing Distributions: too much data to show
Could also make a histogram for each one.
Visualizing Distributions: too much data to show
Whatever you do, don’t combine and colour:
Visualizing Distributions: too much data to show
Better is to use density plots:
Visualizing Distributions: too much data to show
Even better is to use ridge plots:
Who care’s about the density values, anyway?
Visualizing Distributions: too much data to show
Can pack in many categories (ordered, of course).
Visualizing Distributions: too much data to show
You might even be able to get away with colouring by continent. (arguable)
Visualizing Trends
Stacking just doesn’t work.
Visualizing Trends
Putting bars beside each other is better, but still not good.
Visualizing Trends
Best is to forego the bars and use lines:
Visualizing Trends
Bonus: line up the legend.
Activity: Worsen the plot
Code and idea by Firas Moosvi
class: middle, center
Data-to-ink ratio
Overlapping Points
How do you know there aren’t overlapping points here? You don’t.
Overlapping Points
Add some transparency, and suddenly you can tell.
Overlapping Points
Or, jitter the points a little bit.
Overlapping Points
When jittering isn’t an option, and alpha transparency isn’t enough?
Overlapping Points
Consider reducing the size of the points:
Overlapping Points
Or, use hexagonal binning (heatmap):
Colour
Don’t try to choose your own colours.
Colour
Avoid too many colours
Colour
Are you sure you just don’t want to highlight a few or even one category of interest?
Colour Blindness
Previous colour palette with Protanope (reduction of reds):
(Converted by hclwizard)
Colour Blindness
You could try to accommodate colour blindness and still use colour…
Viridis scale
Colour Blindness
Better yet, don’t rely on colour at all. Facet by species:
Notice the axes are comparable.
Choose an Appropriate Scale
Here, the data are pressed against the y-axis. Tons of whitespace.
Choose an Appropriate Scale
Use a log scale on the x-axis.
Activity: Improve the Plot
How can we make this plot better?
Final Remarks: Making plots
- All plots today are reproducible.
- Built with
ggplot2in R. - Take a look at Episode 5 of STAT 545 for an intro to
ggplot2 - Non-reproducible (like Excel) not recommended, but will do if that’s all you know.
